Delay Performance Research Articles

Support Vector Machine (SVM) based Detection for Volumetric Bandwidth Distributed Denial of Service (DVB-DDOS) attack within gigabit Passive Optical Network

The dynamic bandwidth allocation (DBA) algorithm is highly impactful in improving the network performance of gigabit passive optical networks (GPON). Network security is an important component of today’s networks to combat security attacks, including GPON. However, the literature contains reports highlighting its vulnerability to specific attacks, thereby raising concerns. In this work, we argue that the impact of a volumetric bandwidth distributed denial of service (DVB-DDOS) attack can be mitigated by improving the dynamic bandwidth assignment (DBA) scheme, which is used in PON to manage the US bandwidth at the optical line terminal (OLT). Thus, this study uses a support vector machine (SVM), a machine learning approach, to learn the optical network unit (ONU) traffic demand patterns and presents a hybrid security-aware DBA (HSA-DBA) scheme that is capable of distinguishing malicious ONUs from normal ONUs. In this article, we consider the deployment of the HSA-DBA scheme in OMNET++ to acquire the monitoring data samples used to train the ML technique for the effective classification of ONUs. The simulation findings revealed a mean upstream delay improvement of up to 63% due to the security feature offered by the mechanism. Besides, significant reductions for the upstream delay performance recorded at 63% TCONT2, 65% TCONT3, and 95% TCONT4 and for frame loss rate reduction for normal ONU traffic, respectively, were observed in comparison to the non-secure DBA mechanism. This research provides a significant stride towards secure GPONs, ensuring reliable defense mechanisms are in place, which paves the way for more resilient future broadband network infrastructures.

Secure Vehicle-to-Vehicle Communication using Routing Protocol based on Trust Authentication Secure Sugeno Fuzzy Inference System Scheme

Introduction: Vehicular Ad-hoc Network (VANET) is wireless communication between Roadside vehicles and vehicle infrastructure. Vehicle Ad Hoc Network (VANET) is a promising technology that effectively manages traffic and ensures road safety. However, communication in an open-access environment presents real challenges to security and privacy issues, which may affect large-scale deployments of VANETs. Vehicle identification, classification, distribution rates, and communication are the most challenging areas in previous methods. Vehicular communications face challenges due to vehicle interference and severe delays. Method: To overcome the drawbacks, this work proposed a new method based on the Artificial Neural Network Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS). Vehicular Ad Hoc Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators. Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) used the symmetric key to increase the security performance of VANET. Use ANFIS-based Secure Sugeno Fuzzy System for calculating the node weights for data transferring; reduced the attacks accuracy of network malicious attacks. Result: In the improved cluster-based VANET routing protocol, each node obtains an address using a new addressing scheme between the wireless vehicle-2-vehicle (V2V) exchanges and the Roadside Units (RSUs). It will explore the effectiveness of the Secure Sugeno Fuzzy System-based adaptation term Enhanced Cluster-based routing protocol in finding the vehicle's shortest-path for transmission. Conclusion: Simulation results show that in the proposed ANN-based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) analysis, the packet delivery ratio is 93%, delay performance is 0.55sec, throughput performance is 94%, bandwidth is 55bits/sec, Network security is 92%, and the transmission ratio is 89%, attack detection is 90%.

Admission control algorithm for visible light communication random access network under delay and jitter constraints

Abstract This paper proposes a random access network admission control algorithm under delay and jitter constraints to efficiently guarantee the quality of service of a visible light communication system. The multi-packet reception technology is considered to enhance network service, and the interrupted Bernoulli process is used to model the arrival process of packets. The queuing model of the system is constructed from the perspective of a single transmission link, and this paper derives the probability of successful transmission for the terminals. The delay and jitter performance are evaluated according to the state transition matrix and Little’s law. The article set up the optimization problem with the goal of maximizing the throughput and the constraint of transmission probability. To solve the optimization problem better, we improve the tunicate swarm algorithm. Moreover, an admission control algorithm is designed under delay and jitter constraints. Simulation results demonstrate the influence of different parameters on the algorithm.

Novel Low-Power Computing-In-Memory (CIM) Design for Binary and Ternary Deep Neural Networks by Using 8T XNOR SRAM

The increasing demand for high-performance and low-power hardware in artificial intelligence (AI) applications, such as speech recognition, facial recognition, and object detection, has driven the exploration of advanced memory designs. Convolutional neural networks (CNNs) and deep neural networks (DNNs) require intensive computational resources, leading to memory access times and power consumption challenges. To address these challenges, we propose the application of computing-in-memory (CIM) within FinFET-based 8T SRAM structures, specifically utilizing P-latch N-access (PLNA) and single-ended (SE) configurations. Our design significantly reduces power consumption by up to 56% in the PLNA configuration and 60% in the SEconfiguration compared to traditional FinFET SRAM designs. These reductions are achieved while maintaining competitive delay performance, making our approach a promising solution for implementing efficient and low-power AI hardware. Detailed simulations in 7 nm FinFET technology underscore the potential of these CIM-based SRAM structures in overcoming the computational bottlenecks associated with DNNs and CNNs.

High speed and low power hybrid adder using 22 nm SG-TG FinFET

Abstract This paper demonstrates a new 22-T (transistor) hybrid adder for low-power applications using 22 nm shorted gate (SG) tri-gate (TG) FinFET technology. Large conducting channels and increased gate control make FinFET suitable for high-speed low-power computing circuits. The proposed hybrid adder is designed using pass transistor and CMOS logic. Also, it adopts the state-of-the-art techniques of dynamic leakage balancing on the ground path and stacking ground transistors that reduce parasitic effects and increase the speed of the logic transitions compared to traditional designs. Extensive simulations were executed to characterize for PDP (Power Delay Product), power dissipation, and delay of the hybrid adder circuit. From the Monte Carlo analysis, the robustness and efficiency of the circuit are also observable for minimum variation in power and delay due to process voltage and temperature (PVT) variations. Besides, concerning the supply variability and temperature, the circuit shows excellent stability by keeping the power consumption and delay performance consistent. The proposed 22-T hybrid adder using 22 nm SG-TG FinFET are very well-suited in low power and high-speed processors showing highly stable behaviour with reduced process variability.

Availability Evaluation of Cross Project Software Based on GAKT and Supervised Learning in Personalized Innovation Education for College Students

The availability evaluation of cross project software is crucial for personalized innovation education and learning of college students. Ensuring the stability and efficiency of educational resources and tools is the key to improving learning outcomes. This study proposes an evaluation model that integrates MeanShift clustering method and K-nearest neighbor algorithm. The approach enhanced the feature selection of the model and improved the model by combining attention mechanism. The experimental results showed that the proposed model performed well in multiple aspects. In addition to exhibiting superior convergence performance, the proposed model also demonstrated a significant advantage over naive Bayesian and gradient enhanced decision tree models in terms of delay performance. The optimal delay was 2.47s, and the data volume was as low as 14.12MB. Moreover, its accuracy reached 94.11%, which was 6.61% and 19.11% higher than naive Bayesian and gradient enhanced decision trees, respectively. This model was of practical significance for optimizing the software tools for personalized and innovative education and learning of college students. The MeanShift K-nearest neighbor model can provide educational institutions with a more reliable and efficient evaluation tool. The proposed model can better adapt to and meet the personalized learning needs of students, while also reducing potential learning interruptions caused by software unavailability. In addition, the efficient data processing and accuracy performance of the model means that high-quality availability assessments can also be achieved in resource constrained environments.

Load adaptive multi-threshold scheduling to concurrently improvise transmission time and wait time performance of C-RAN

Low-latency requirements of 5G networks are one of the fundamental needs to support delay-sensitive applications. Uplink scheduling mechanism at wired midhaul of Centralized Radio Access Network (C-RAN) architecture significantly affects the transmission speed and delay performance of the network. Inadequate performance of conventional scheduling algorithms necessitates improvements to attain these requirements. In this paper, load adaptive scheduling with single threshold (LAS-ST) and load adaptive scheduling with multi-threshold (LAS-MT) algorithms on C-RAN architecture are proposed to reduce transmission time and wait time parameters concurrently. The proposed algorithms have been compared with two conventional scheduling algorithms, multi-scheduling domain (MSD) & single scheduling domain (SSD) and applied at fronthaul and midhaul of C-RAN for its comprehensive analysis. MSD and SSD are widely used in Ethernet Passive Optical Network (EPON) architectures. The results confirm that the proposed multi-threshold scheduling algorithm performs best in meeting the objectives of the studycompared to the existing scheduling algorithms in terms of transmission time and wait time by adapting to conditions. Further, resource utilization and resource availability are observed for several transmission cycles (TCs). In addition, data load variation and variation in number of available resources is analysed and its effect on transmission time and wait time is studied.

Research on the Technology and Application of Remote Wireless Transmission of Digital Image Signal

With the increasing demand for remote real-time high-quality video transmission, especially in the context of large area applications of unmanned aerial vehicle (UAV) and remote location instant live broadcast, traditional wired transmission methods have been surpassed by digital transmission methods due to their superior performance in image quality, transmission distance and delay. Therefore, this study focuses on the application of digital image wireless transmission in remote scenarios such as UAVs, including key digital transmission technologies such as Orthogonal Frequency Division Multiplexing (OFDM) and Coded Orthogonal Frequency Division Multiplexing (COFDM), as well as public network link communication technologies such as 4G and 5G, by means of a literature study and a case study. It also analyzes the development of WiFi, Lightbridge and OcuSync graphics transmission systems using DJI brand products as case studies. The analysis shows that digital image wireless transmission, especially COFDM and advanced proprietary systems such as DJI's OcuSync, has significant advantages in terms of transmission distance, interference immunity, encryption techniques, and reusability. However, factors such as building obstruction, frequency interference, and weather conditions can affect the transmission quality. To mitigate these challenges, solutions are proposed, such as auxiliary signal enhancement systems, adaptive frequency hopping and software-defined radio technologies.

Retransmission Aware Adaptive Modulation and Coding Toward Deterministic Delay Performance

Retransmission Aware Adaptive Modulation and Coding Toward Deterministic Delay Performance

Compact design of universal gates with non-aligned gate technology and assessment of its performance in a TCAD Environment

ABSTRACT This paper proposes a technology computer-aided design (TCAD) of a compact single-device NAND and NOR logic gates in 215 nm device length along with speed and power performance results. The universal gates are designed with non-aligned double gate technology, and this helps to reduce the transistor count, giving a compact design structure. The static characteristics and input pattern sensitivity of the NAND and NOR structures are investigated in this work. By using graphical method, the optimal supply voltage of the universal gates was determined. The optimal supply voltage of the NAND gate and NOR gate was 0.9 V and 0.84 V, respectively, at an input frequency of 1 GHz. Further, the device scalability of the proposed structures was analysed. When compared to the past work, the proposed NAND gate and NOR gate improves delay performance by 25.02% and 35.63%, respectively. Additionally, a reduction of 10% and 16% in its supply voltage is achieved. Therefore, the proposed gate has potential to operate at higher frequency with reduced supply voltage, making them suitable in circuit applications.

Highly narrowband miniaturized microstrip diplexer for qubit mobile communication networks utilizing SIR and T-shaped resonators with negative group delay performance

Highly narrowband miniaturized microstrip diplexer for qubit mobile communication networks utilizing SIR and T-shaped resonators with negative group delay performance

Capacity and delay performance analysis for large-scale UAV-enabled wireless networks

In this paper, we analyze the capacity and delay performance of a large-scale Unmanned Aerial Vehicle (UAV)-enabled wireless network, in which untethered and tethered UAVs deployed with content files move along with mobile Ground Users (GUs) to satisfy their coverage and content delivery requests. We consider the case where the untethered UAVs are of limited storage, while the tethered UAVs serve as the cloud when the GUs cannot obtain the required files from the untethered UAVs. We adopt the Ornstein-Uhlenbeck (OU) process to capture the mobility pattern of the UAVs moving along the GUs and derive the compact expressions of the coverage probability and capacity of our considered network. Using tools from martingale theory, we model the traffic at UAVs as a two-tier queueing system. Based on this modeling, we further derive the analytical expressions of the network backlog and delay bounds. Through numerical results, we verify the correctness of our analysis and demonstrate how the capacity and delay performance can be significantly improved by optimizing the percentage of the untethered UAVs with cached contents.

Adaptive predefined-time consensus for nonlinear multi-agent systems with input delay and performance constraints

This paper investigates the predefined-time tracking consensus issue for a class of non strict-feedback nonlinear multi-agent systems (MASs) under input delay and error performance constraints by adaptive backstepping control strategy. Firstly, the impact of input delay is eliminated by Pade approximation. With the help of an intermediate state variable, the considered MASs with input delay are revised as the independent-delay systems. Secondly, under constrained distributed error and corrected nth virtual error, the ln-type barrier Lyapunov function (BLF) is adopted to solve the error performance constrains and reduce the complexity of differentiation. Meanwhile, an adaptive predefined-time controller is designed to realize the tracking purpose by backstepping method and radial basis function neural networks (RBFNNs) approximation. Finally, a numerical simulation example and a practical application are presented to test the effectiveness for the proposed control strategy.

Patient delay, diagnosis delay, and treatment outcomes among migrant patients with tuberculosis in Shanghai, China, 2018-2020: a mixed-methods study

ObjectivesThis study aimed to examine patient delay, diagnosis delay and treatment performance among patients with tuberculosis (TB) in Shanghai, China in 2018–2020 focusing on disparities between migrant and local patients...

The influence of e-learning on exam performance and the role of achievement goals in shaping learning patterns

Digital learning environments provide opportunities to support learning in higher education. However, it is yet unclear why and how learners use these opportunities. We propose that learners’ achievement goals and their beliefs regarding the instrumentality of e-learning tools to achieve those goals are predictive for learning behavior within digital learning environments. Furthermore, we assume learning behavior characterized by longer overall learning time, more distributed learning, and less learning delay predicts higher exam performance. To test these hypotheses, we analyzed log-file data of 91 university students who had used an intelligent tutoring system as exam preparation in a pre-registered study. Beyond the overall predictive validity of the intelligent tutoring system, we found a negative association between learning delay and exam performance. Achievement goals predicted learning time and time distribution, an association that was partly moderated by perceived instrumentality. This suggests that goals and beliefs are important puzzle pieces for understanding e-learning (behavior).

Throughput and Delay Performance of Slotted Aloha in SmartBANs Under Saturation Conditions

Throughput and Delay Performance of Slotted Aloha in SmartBANs Under Saturation Conditions

Frequency-domain analysis of CMOS-driven interconnects utilizing doped multilayer graphene nanoribbons and mixed carbon nanotube bundles

A frequency-domain model is developed to analyze isolated interconnects of multilayer graphene-nanoribbon (MLGNR) and mixed carbon-nanotube bundle (MCB) driven by CMOS gates. The model derived is founded on an equivalent-single-conductor model of MLGNR and MCB that takes thermal considerations into account (i.e. TD-ESC). The model includes the derivation of transfer function of interconnect to estimate its delay and bandwidth performance. The attained results, reveals that among the neutral MLGNR (N-MLGNR), intercalation doped MLGNR (ID-MLGNR) intercalated with FeCl3, MCB and Cu interconnects, FeCl3 ID-MLGNR achieves the best bandwidth efficiency. At a global interconnect length of 1 mm, FeCl3 ID-MLGNR outperforms N-MLGNR, MCB, and Cu in terms of bandwidth with an improved bandwidth value of 12.2 GHz, 7 GHz, and 61.4 GHz, respectively. Further, employing the proposed CMOS-gate-driven model, for FeCl3 ID-MLGNR, bandwidth is improved by nearly 7.52 × at global length (∼1 mm) in relation to the linear resistance model. Additionally, TD-ESC dependency of the proposed model reveals that FeCl3 ID-MLGNR becomes more stable as interconnect resistance increases.

Sharp Waiting-Time Bounds for Multiserver Jobs

Multiserver jobs, which are jobs that occupy multiple servers simultaneously during service, are prevalent in today’s computing clusters. But, little is known about the delay performance of systems with multiserver jobs. We consider queueing models for multiserver jobs in scaling regimes where the system load becomes heavy and meanwhile, the total number of servers in the system and the number of servers that a job needs become large. Prior work has derived upper bounds on the queueing probability in this scaling regime. However, without proper lower bounds, the existing results cannot be used to differentiate between policies. In this paper, we study the delay performance by establishing sharp bounds on the steady-state mean waiting time of multiserver jobs, where the waiting time of a job is the time spent in queueing rather than in service. We first characterize the exact order of the mean waiting time under the first come, first serve (FCFS) policy. Then, we prove a lower bound on the mean waiting time of all policies, which has an order gap with the mean waiting time under FCFS. We show that the lower bound is achievable by a priority policy that we call smallest need first (SNF). Funding: This research was supported in part by the National Science Foundation [Grant ECCS-2145713]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsy.2023.0006 .

Cloud-edge collaborative high-frequency acquisition data processing for distribution network resilience improvement

To realize transparent monitoring and resilience improvement of low-voltage distribution network, both the data acquisition scope and frequency have been greatly expanded. Cloud-edge collaboration leverages the edge server’s real-time response capabilities and the cloud server’s robust data processing power to enhance the performance of high-frequency data acquisition processing. Nonetheless, it continues to confront challenges such as the entanglement of optimization variables, the presence of uncertain information, and a lack of awareness regarding acquisition frequencies. In this paper, we propose a machine learning-based cloud-edge collaborative data processing optimization algorithm to minimize the weighted sum of data processing delay and device energy consumption for distribution network resilience improvement. The joint optimization problem is decoupled into device-edge data offloading subproblem and edge-cloud data splitting subproblem, which are solved by the proposed upper confidence bound (UCB) based frequency-aware device-edge data offloading optimization algorithm and the exponential-weight algorithm for exploration and exploitation (EXP3) based edge-cloud data splitting optimization algorithm, respectively. Simulation results show that the proposed algorithm is superior to existing algorithms in performances of energy consumption and total processing delay.

Delay-Oriented Roadside Unit Deployment for Highway Intersections in Vehicular Ad Hoc Networks.

Optimizing the deployment of roadside units (RSUs) holds great potential for enhancing the delay performance of vehicular ad hoc networks. However, there has been limited focus on devising RSU deployment strategies tailored specifically for highway intersections. In this study, we introduce a novel probabilistic model to characterize events occurring around highway intersections. By leveraging this model, we analytically determine the expected event reporting delays for both highway segments and intersections. Subsequently, we propose an RSU deployment scheme specifically designed for highway intersections, aimed at minimizing the expected event reporting delay. To implement this scheme, we introduce an innovative algorithm named cooperative walking. Through illustrative examples, we demonstrate that our proposed RSU deployment strategy for highway intersections outperforms the commonly employed uniform RSU deployment scheme and the previously proposed balloon method in terms of delay performance.